726results about "Auscultation instruments" patented technology

The invention relates to the technical field of heart sound signal processing, and discloses a heart sound feature recognition method based on wavelet spectral analysis. The method comprises the following steps: collecting heart sound signal data and carrying out signal filtering and segmentation processing to obtain segmented cardiac cycle signals; performing n-level discrete wavelet transform decomposition to obtain an approximation coefficient and n detail coefficients; performing multi-dimensional statistical feature calculation to obtain a multi-dimensional feature vector; performing principal component analysis dimension reduction processing to obtain a dimension reduction feature vector; fitting feature distribution by using a Gaussian mixture model and introducing a dynamic adjustment factor to obtain an adaptive classification threshold; the hidden Markov model is constructed through the dimension reduction feature vectors, the likelihood probability is calculated through a forward algorithm, the likelihood probability is compared with a self-adaptive classification threshold value, and the heart sound feature recognition result is output. The time-frequency features of the heart sound signals are fully utilized, the feature distinction degree and the recognition accuracy are improved, and the recognition accuracy is improved. And meanwhile, the method has good robustness and generalization ability.

The invention relates to a ventilation tube (1) for ventilating a patient, comprising a tube (2) surrounding an air channel (25) with a tube wall (3) for insertion into a throat or trachea of the patient, a cuff (4, 15) with an air volume (6) circulating in the circumferential direction of the tube wall (3), and a pump line (5) running in the axial direction of the tube (2) for establishing a flow connection between the air volume (6) of the cuff (4, 15) and a pump device (7). In order to be able to detect conditions relating in particular to the breathing or ventilation of the patient with high quality, it is proposed that the ventilation tube (1) have an acoustic sensor (8, 16) arranged in the air volume (6) on the tube wall (3) or in a partial region of the cuff (4, 15) adjacent to the air channel (25).

Obstructive sleep apnea from blockage of the upper airway can result in significant health issues. Described herein is an intelligent personalized closed loop neuromodulation system and methods to prevent backwards movement of the genioglossus muscle through stimulation of certain branches of the hypoglossal nerve and muscle motor points, by using muscle feedback from electromyogram sensors to provide optimal stimulus. Alternative embodiments to treat neuropathic pain and urinary dysfunction are enclosed.

Disclosed herein is a multi-modal sensor system, including a wearable device configured to receive signals relating to a swallowing process of a subject, the wearable device including one or more surface Electromyograph sensors configured to receive signals relating to electrical potential in muscles of the throat, one or more bio-impedance sensors, one or more memories, one or more processors configured to operate one or more sensors of the wearable device, synchronize the signals to one or more predetermined events to generate a synchronization feature, receive the signals as a first diagnostic data set, analyze the first diagnostic data set, assess, based on the analysis, the swallowing process of the subject to yield an assessment output, and present the assessment output and determine a bio-impedance signal.

A wearable device may be worn in an ear of a user. The wearable device may include a biometric sensor that collects biometric information from the ear, and an environmental sensor that collects environmental information from the ear. The wearable device may receive the biometric information from the biometric sensor, and the environmental information from the environmental sensor, and send biofeedback determined from the biometric information, and an environmental condition determined from the environmental information, wirelessly to a communication device. Other embodiments are disclosed.

Coaptation assist device for repairing cardiac valves and associated systems and methods are disclosed herein. A coaptation assist device configured in accordance with embodiments of the present technology can include, for example, a fixation member configured to press against cardiac tissue proximate to a native valve annulus, and a stationary coaptation structure extending away from the fixation member. The coaptation structure can include an anterior surface configured to coapt with a first native leaflet during systole and a posterior surface configured to displace at least a portion of a second native leaflet. The device also includes at least one sensor configured to detect parameters associated with at least one of cardiac function and device functionality. The sensors can be pressure sensors configured to detect left atrial pressure and / or left ventricular pressure.

A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

The invention discloses a sleep apnea two-stage detection method, and relates to the field of respiration detection, and the sleep apnea two-stage detection method comprises the following steps: collecting a trachea sound signal of sleep respiration of a user, and generating a Mel spectrum of the trachea sound signal; wherein the trachea sound signal comprises a breathing audio signal and a heart sound signal; constructing a sleep-awakening detection model based on a VGG16 network, and calculating the sleep duration of the user; performing sleep apnea section detection on the Mel spectrum by adopting a longitudinal box algorithm AV-Box; extracting S1 peaks and S2 peaks of the potential sleep apnea section, and calculating an RR interval according to the time interval of the adjacent S1 peaks; acquiring statistical characteristics of the RR interval, and performing apnea detection by using a support vector machine (SVM) according to the statistical characteristics; and calculating the sleep apnea hypopnea index AHI of the user according to the sleep apnea times and the sleep duration counted according to the apnea detection result. Aiming at low diagnosis precision in a sleep apnea detection method based on a sound signal in the prior art, the detection precision is improved.

The invention discloses an intelligent atomization control method and system for respiratory tract treatment and a storage medium, and relates to the technical field of atomization control. During operation of the system, a multi-mode patient respiratory behavior monitoring module is used for capturing real-time respiratory frequency, depth, rhythm, cough characteristics and atomization efficacy based on a non-contact sonic sensor and lung sound analysis; the method comprises the following steps: monitoring environment humidity, temperature and aerosol diffusion rate in real time, analyzing the influence of the environment humidity, temperature and aerosol diffusion rate on drug mist particle size and sedimentation behavior, controlling atomization particle size through a variable-frequency ultrasonic exciter, calculating to obtain an intelligent atomization curative effect adaptation index AEAI, dynamically adjusting particle size distribution according to the real-time inhalation demand of a patient and environment parameters, and combining with respiration monitoring data to obtain the intelligent atomization curative effect adaptation index AEAI. According to the method, the medicine mist release opportunity is adjusted in real time, threshold value comparison is carried out by monitoring respiratory resistance changes and real-time feedback of lung sound characteristic improvement degree, a medicine release strategy is obtained, and remote doctor viewing and parameter adjusting functions are provided.

A diagnostic system includes one or more support surfaces, on which a person to be examined can be positioned. To be able to make a diagnosis without specialist staff, electrodes are provided to be arranged on various regions of the support surface, particular on leg regions, on arm regions, on an upper body region and / or on a neck region. These electrodes are at least partly embodied as electrode pairs, wherein each electrode pair has a feed electrode and / or a measurement electrode. The feed electrode is arranged distally relative to the measurement electrode. A device is provided for feeding an electric current between the feed electrodes and the diagnostic system is configured to measure a voltage drop between the measurement electrodes in order to be able to determine impedances of body regions of a person located on the diagnostic system.

The present invention discloses an intelligent respiratory support and airway clearance auxiliary system, method, device and storage medium, which relates to the field of medical data processing technology. The data acquisition module is used to obtain real-time parameter data; the respiratory analysis unit analyzes the blood oxygen saturation, oxygen partial pressure and carbon dioxide partial pressure data; the lung image data analysis unit intelligently analyzes the lung auscultation image data to identify the lung sputum blockage; the ventilator waveform analysis unit identifies abnormal waveform data based on the waveform data of the ventilator; the airway clearance module is used to determine whether it is necessary to start the Ilohen expectoration breathing mode according to the intelligent analysis results. The present invention uses advanced data processing technology and algorithms to monitor the patient's breathing in real time, and promptly discover and deal with airway problems. Through systematic data collection and analysis, the efficiency and accuracy of airway clearance are improved, the patient's treatment effect is improved, and the patient can actively expectorate while using a ventilator.

A method of identifying a patient with obstructive sleep apnea implants a phrenic nerve stimulator in the patient or connects the phrenic nerve stimulator to the diaphragm, and adjusts the stimulation energy applied by the phrenic nerve stimulator to the phrenic nerve of the sleeping patient based on airway occlusion in the airway of the patient.

The invention discloses an abnormal data detection method, device and system based on electrocardio and heart sound, and the method comprises the steps: synchronously collecting an electrocardio signal and a heart sound signal, and carrying out the first preprocessing and the second preprocessing, thereby obtaining electrocardio data and heart sound data; inputting the electrocardio data and the heart sound data into a double-flow convolutional network for feature extraction to obtain an electrocardio feature sequence and a heart sound feature sequence; inputting the electrocardio feature sequence and the heart sound feature sequence into a cross-modal fusion decision model to obtain pooling fusion enhanced features, thereby effectively performing time sequence alignment, fusion and enhancement on the electrocardio feature sequence and the heart sound feature sequence; the score of the pooling fusion enhanced feature is obtained by using a nonlinear scoring formula, whether abnormal data exist in the electrocardiosignal and the heart sound signal of the detected person or not is determined through the score of the pooling fusion enhanced feature, and the method has important significance on early warning, curative effect evaluation and the like of patients with cardiovascular and cerebrovascular diseases.

Monitoring sleep disorders in a subject using a mobile device, such as a smartphone or other wearable device, includes: co-locating the mobile device with the subject; acquiring an audio signal indicative of the respiration of the subject by means of a microphone of the mobile device; acquiring an accelerometer signal including posture, angle and motion information of the tested object by means of an accelerometer of the mobile device; measuring thoracic movements associated with respiration and respiration of sleep disorders by means of an accelerometer of the mobile device; identifying a plurality of silence events by means of the audio signal; classifying the silence event as apnea or hypopnea; classifying the apnea into a plurality of apnea types by means of thoracic motion recorded with the accelerometer sensor; and associating the classified silence events with respective angle information by combining audio and accelerometer signals.

The subject of the invention is a method of electroacoustic heart-lung dynamics that includes the steps of: A) recording a three-dimensional ECG; B) recording three-dimensional impedance rheometry of the chest; C) recording of chest sounds resulting from internal organs in the frequency range up to 20 kHz; D) recording ultrasound from the chest resulting from the movement of internal organs in the frequency range above 20 kHz; E) determination of characteristic events in the data from steps A), B), C), and D) associated with a given organ in particular detection of the breathing phase, breathing rhythm, and breathing rate, and in particular detection of the heart contraction phase using the data from steps A) and B), and detection of sounds from the lungs; F) determining the temporal and / or phase correlation of events in the data from steps A), B), C) and D); G) comparing the temporal and / or phase correlation of events from steps A), B), C), and D) with the benchmark data; H) determination of the relationship between the determined temporal and / or phase correlation of events and the benchmark temporal and / or phase correlation of events. The object of the invention is also a device for implementing this method. (15 claims)

Systems, methods, and devices include a neurological function assessment system involving a first assessment tool (e.g., hammer) with one or more first sensors. The system includes a second assessment tool being a wearable device operable to wrap around a body part of a subject. One or more second sensors are disposed on the wearable device operable to sense a target area at the body part. The system collects first sensor data, from the first assessment tool, corresponding to an assessment event at the first assessment tool; and collects second sensor data, from the second assessment tool, corresponding to the assessment event. Additionally, the system presents, at a display, an indication of an objective neurological assessment parameter value calculated based on the first sensor data and the second sensor data. An objective neurological assessment parameter value can be calculated based on the first sensor data and / or the second sensor data.

The invention discloses a thoracic cavity heart sound signal non-contact extraction system and method based on structured light. The system comprises a structured light projection module, an image acquisition module, an image processing and displacement extraction module and a signal processing and trigger point identification module, structured light is projected on the surface of the chest of a human body to form a regular pattern, and a high-speed image acquisition means is utilized to record micro displacement change generated by light spots under cardiac drive. Thoracic cavity vibration information is extracted through an image processing technology, periodic feature points corresponding to a cardiac rhythm are recognized in combination with a time sequence analysis strategy, a thoracic cavity heart sound signal is extracted, rhythm analysis is conducted, and a cardiac trigger point is output to serve as a trigger signal for synchronous control of medical equipment. The technical problems of signal distortion, equipment interference, potential safety hazards, inconvenience in use and the like in a strong electromagnetic environment in a traditional contact mode are solved, and a high-precision synchronous trigger signal is provided for dynamic imaging.

Systems, devices, and methods including a processor having addressable memory, where the processor is configured to: collect, via a set of sensors, biological signals related to patient status; collect, via an environmental monitoring device, environmental data associated with an environment of the patient; store, locally via a secure encrypted local database, the collected biological signals and the collected environmental data; detect an abnormality in the collected biological signals; increase measurement frequency of the biological signals by the set of sensors based on the detected abnormality; detect an error in the collected biological signals; determine whether the detected error in the collected biological signals of the current reading is due to environmental factors; correct the determined error in the collected biological signals; and transmit the error corrected collected biological signals as encrypted data.

The present disclosure relates to methods and systems for determining exercise related ventilatory data of a person (8), in particular determining one or more ventilatory thresholds of the person (8) and / or determining a ventilatory zone of the person (8). Microphone signals received from a microphone (2A, 2B, 2C) associated with the person (8) are received and analyzed to determine the breathing rate, and using the breathing rate the ventilatory threshold(s) and ventilatory zone(s) are determined.

An anti-snoring system comprising an adjustable bed having a sleeping surface that may be mechanically raised or lowered, and a control module adapted to receive commands from a source external to the adjustable bed; a mobile device in direct or indirect communication with the control module in the adjustable bed, and wherein the mobile device has sound recording capabilities; a mobile application resident on the mobile device, wherein the mobile application includes a sound classification machine learning model that includes an artificial intelligence or neural network operative to determine whether or not a person on the sleep surface is snoring, and wherein upon a determination that the person is snoring and has been snoring for a predetermined period of time, the mobile application instructs the control module to raise or adjust the sleeping surface to a height or position that will discourage the person from snoring.

The disclosure provides systems and methods for treating obstructive sleep apnea using an acoustic sensor configured to detect acoustic sounds generated by the heart and lungs. Sensory data from the acoustic sensor is used by an implanted stimulation system to determine when to deliver electrical stimulation to a nerve which innervates an upper airway muscle, such as the hypoglossal nerve, to treat sleep apnea.

The invention relates to the technical field of respiratory lung sound recognition, in particular to a respiratory lung sound auxiliary recognition method and system for clinical nursing. The invention provides a respiratory lung sound auxiliary identification method and system for clinical nursing, solves the technical problems of data scarcity, complex noise interference and insufficient cross-device generalization ability in lung sound signals through generative data enhancement and cross-modal migration, and combines self-supervised comparative learning and causal feature discovery to identify the respiratory lung sound. Multi-source signal feature fusion and noise robustness improvement are realized, a real-time noise environment is dynamically adapted, the limitation that a traditional method depends on annotation data and feature extraction is easily influenced by hybrid noise is broken through, a full-link closed-loop system from synthetic data generation, cross-modal alignment to causal-driven decision is constructed, and multi-source signal feature fusion and noise robustness improvement are realized. And the accuracy and clinical applicability of clinical lung sound analysis are effectively improved.

A patient monitor for displaying a physiological signal can include a visual element having a middle portion indicative of a transition in the physiological signal between physiological states. The visual element can also include first and second extremity portions, the first extremity portion extending from the middle portion in a first direction and the second extremity portion extending from the middle portion in a second direction. The visual element can also include an actionable value indicator to specify a value about the middle portion and the first and second extremity portions. The patient monitor can also include a processor configured to cause the value indicator to actuate in both the first and second directions according to changes in the physiological signal.

The invention relates to the technical field of mattresses, in particular to a multifunctional heart failure patient condition monitoring mattress which comprises a mattress body. The mattress further comprises an adjusting mechanism, a monitoring mechanism and an analysis processing module, and the adjusting mechanism, the monitoring mechanism and the analysis processing module are all mounted on the mattress main body; the monitoring mechanism is used for monitoring the physical condition of a patient, the adjusting mechanism is used for adjusting the heights of different positions of the mattress main body, and the analyzing and processing module is used for analyzing and processing data acquired by the monitoring mechanism; a patient lies on the mattress body, the physical condition of the patient is monitored through the monitoring mechanism, then data obtained by the monitoring mechanism is analyzed and processed through the analysis and processing module, and the adjusting mechanism is controlled to adjust the heights of different positions of the mattress body, so that the shape of the mattress body is suitable for the patient, and the return blood volume is reduced; the comfort of the patient is improved, and the morbidity of the patient is reduced.

The invention discloses an intelligent intestinal movement recognition method and system based on BRS acceleration, and relates to the field of medical detection, and the method comprises the steps: obtaining the abdomen feature data of a target patient in a target time period, and obtaining a target observation signal; the abdominal feature data comprises abdominal wall vibration data collected by a BRS acceleration sensor; performing blind source separation operation on the target observation signal to obtain a target source signal; identifying a vibration source category corresponding to each target source signal to screen a target intestinal component; extracting a feature vector of the target intestinal motion component to obtain a target feature vector; the target feature vector is matched with a preset normal value, when the target feature vector does not conform to the preset normal value, a first feedback signal is generated, noninvasive detection of the intestinal movement of the patient is achieved, and the method can be used for long-time continuous monitoring of the intestinal movement of the patient.

A system and method for determining a measurement of patient weight using existing medical device sensors is disclosed, the system and method including receiving patient acceleration information, detecting patient steps within a measurement window using the acceleration information when a value of the acceleration information exceeds an activity threshold over the measurement window, determining a patient step count over the measurement window using the detected patient steps, determining a measurement of patient walking force in the measurement window, and determining a measurement of patient weight using the determined patient step count and the measurement of patient walking force.

This invention pertains to a portable device for individualised measurements of energy expenditures and calculating energy intakes and balances of humans and air-breathing animals. Said device utilises algorithms that use a relation between the cumulative energy expenditure and the pulmonary and the cutaneous respirations of a user of said device; and a relation between said pulmonary tidal volume and at least one of the respiration and the hearth frequencies of said user; and a relation between the cumulative energy intake and the masses of carbohydrates, fats and proteins in the dietary intake of said user; wherein the energy balance of said user is the difference between the cumulative energy expenditure and the cumulative energy intake. Data received from a pulse frequency sensor is sufficient for routine operation of said device.

A system and method for monitoring heart function based on heart sounds (HS) is provided. The system includes electrodes configured to sense electrical cardiac activity (CA) signals over a period of time. An HS sensor is configured to sense HS signals over the period of time. The system includes memory to store specific executable instructions and includes one or more processors that, when executing the specific executable instructions, is configured to: identify a characteristic of interest (COI) of a heartbeat from the CA signals. The processors overlay a HS search window onto an HS segment of the HS signals based on the COI from the CA signals and calculate a center of mass (COM) for at least one of S1 or S2 HS based on the HS segment of the HS signals within the search window to obtain a corresponding at least one of S1 COM or S2 COM. The processors calculate at least one of an electromechanical activation time (EMAT) or a systolic interval (SI) based on the at least one of S1 COM or S2 COM and record the at least one of the EMAT or SI.

Systems, methods, and devices include a platform with a transparent material which defines a first measurement surface. Light surfaces are positioned to provide light into the transparent material and cause a Frustrated Total Internal Reflection (FTIR) event responsive to contact at the first measurement surface. The system also includes one or more wearable devices having a base material operable to cover a portion of a human body. An inner surface of the base material defines a second measurement surface. Furthermore, one or more sensors disposed on the base material are operable to collect data at the second measurement surface. Additionally, the health parameter monitoring system presents at the display, such as a virtual reality (VR) headset, locomotion regimen information. The system collects first health parameter data from the first measurement surface based on the FTIR event; and / or second health parameter data from the second measurement surface.

An electronic device may monitor, using a microphone, feeding sounds associated with an individual. Then, the electronic device may analyze the monitored feeding sounds, where the analysis uses a time-series shotgun sequencing technique. Next, the electronic device may detect, using a pretrained predictive model, a potential aerodigestive disorder based at least in part on the analyzed monitored feeding sounds. Note that the analyzed monitored feeding sounds may correspond to suck-swallow-breathe (SSB) coordination. Alternatively or additionally, the analyzed monitored feeding sounds may correspond to aspiration or dysfunctional phases of swallowing.